CSEP 517 Natural Language Processing Autumn 2015 Introduction Yejin Choi Slides adapted from Dan Klein, Luke Zettlemoyer
People & Page § Page: § http://courses.cs.washington.edu/courses/csep517/15au/ § People: § Instructor: Yejin Choi § TAs: Ignacio “nacho” cano & James Ferguson
Study Aid
What is NLP? § Fundamental goal: deep understand of broad language § Not just string processing or keyword matching § End systems that we want to build: § Simple: spelling correction, text categorization… § Complex: speech recognition, machine translation, information extraction, sentiment analysis, question answering… § Unknown: human-level comprehension (is this just NLP?)
Machine Translation § Translate text from one language to another § Recombines fragments of example translations § Challenges: § What fragments? [learning to translate] § How to make efficient? [fast translation search] § Fluency (second half of this class) vs fidelity (later)
2013 Google Translate: French
2013 Google Translate: Russian
Jeopardy! World Champion US Cities: Its largest airport is named for a World War II hero; its second largest, for a World War II battle.
Information Extraction § Unstructured text to database entries New York Times Co. named Russell T. Lewis, 45, president and general manager of its flagship New York Times newspaper, responsible for all business-side activities. He was executive vice president and deputy general manager. He succeeds Lance R. Primis, who in September was named president and chief operating officer of the parent. Person Company Post State Russell T. Lewis New York Times president and general start newspaper manager Russell T. Lewis New York Times executive vice end newspaper president Lance R. Primis New York Times Co. president and CEO start § SOTA: perhaps 80% accuracy for multi-sentence temples, 90%+ for single easy fields § But remember: information is redundant!
Knowledge Graph: “ things not strings”
Question Answering Question Answering: § More than search § Can be really easy: § “What’s the capital of Wyoming?” Can be harder: “How § many US states’ capitals are also their largest cities?” Can be open ended: § “What are the main issues in the global warming debate?” Natural Language § Interaction: Understand requests and § act on them “Make me a reservation for § two at Quinn’s tonight’’
Mobile devices can now answer (some or our) questions and execute commands...
Will this Be Part of All Our Home Devices? https://www.youtube.com/watch? v=KkOCeAtKHIc https://www.youtube.com/watch?v=qGU- SqUTees • Why are these appearing now? • What are fundamental limitations in current art?
Language Comprehension?
Speech Recognition § Automatic Speech Recognition (ASR) Audio in, text out § SOTA: 0.3% error for digit strings, 5% dictation, 50%+ TV § “Speech Lab” § Text to Speech (TTS) Text in, audio out § SOTA: totally intelligible (if sometimes unnatural) §
Analyzing public opinion, making political forecasts Today: In 2012 election, automatic sentiment analysis actually being • used to complement traditional methods (surveys, focus groups) Past: “Sentiment Analysis” research started in 2002 or so • Future: Growing research toward computational social science, • digital humanities (psychology, communication, literature and more) Challenge: Need statistical models for deeper semantic • understanding --- subtext, intent, nuanced messages
Summarization § Condensing documents Single or § multiple docs Extractive or § synthetic Aggregative or § representative § Very context- dependent! § An example of analysis with generation
Start-up Summly à Yahoo! CEO Marissa Mayer announced an update to the app in a blog post, saying, "The new Yahoo! mobile app is also smarter, using Summly’s natural-language algorithms and machine learning to deliver quick story summaries. We acquired Summly less than a month ago, and we’re thrilled to introduce this game-changing technology in our first mobile application.” Launched 2011, Acquired 2013 for $30M
Can a robot write news? Despite an expected dip in profit, analysts are generally optimistic about Steelcase as it prepares to reports its third-quarter earnings on Monday, December 22, 2014. The consensus earnings per share estimate is 26 cents per share. The consensus estimate remains unchanged over the past month, but it has decreased from three months ago when it was 27 cents. Analysts are expecting earnings of 85 cents per share for the fiscal year. Revenue is projected to be 5% above the year-earlier total of $784.8 million at $826.1 million for the quarter. For the year, revenue is projected to come in at $3.11 billion. The company has seen revenue grow for three quarters straight. The less than a percent revenue increase brought the figure up to $786.7 million in the most recent quarter. Looking back further, revenue increased 8% in the first quarter from the year earlier and 8% in the fourth quarter. The majority of analysts (100%) rate Steelcase as a buy. This compares favorably to the analyst ratings of three similar companies, which average 57% buys. Both analysts rate Steelcase as a buy. Steelcase is a designer, marketer and manufacturer of office furniture. Other companies in the furniture and fixtures industry with upcoming earnings release dates include: HNI and Knoll.
Writer-bots for earthquake & financial reports Some of the formulaic news articles are now written by computers. • Definitely far from “Op- ed” • Can we make the generation engine statistically learned rather than engineered?
Language and Vision “Imagine, for example, a computer that could look at an arbitrary scene anything from a sunset over a fishing village to Grand Central Station at rush hour and produce a verbal description. This is a problem of overwhelming difficulty, relying as it does on finding solutions to both vision and language and then integrating them. I suspect that scene analysis will be one of the last cognitive tasks to be performed well by computers” -- David Stork (HAL’s Legacy, 2001) on A. Rosenfeld’s vision
What begins to work (e.g., Kuznetsova et al. 2014) The flower was so vivid and attractive. Blue flowers are running We sometimes do well: 1 out of 4 times, machine rampant in my garden. captions were preferred over the original Flickr captions: Spring in a white dress. Blue flowers have no scent. Small white flowers have no idea what they are. Scenes around the lake on my bike ride. This horse walking along the road as we drove by.
But many challenges remain (better examples of when things go awry) Yellow ball suspended in water. The couch is definitely bigger than it looks in this photo. Incorrect Object Recognition Incorrect Incorrect Scene Composition Matching My cat laying in my duffel bag. A high chair in the trees.
NLP History: pre-statistics § (1) Colorless green ideas sleep furiously. § (2) Furiously sleep ideas green colorless § It is fair to assume that neither sentence (1) nor (2) (nor indeed any part of these sentences) had ever occurred in an English discourse. Hence, in any statistical model for grammaticalness, these sentences will be ruled out on identical grounds as equally "remote" from English. Yet (1), though nonsensical, is grammatical, while (2) is not.” (Chomsky 1957) § 70s and 80s: more linguistic focus § Emphasis on deeper models, syntax and semantics § Toy domains / manually engineered systems § Weak empirical evaluation
NLP: machine learning and empiricism “Whenever I fire a linguist our system performance improves.” –Jelinek, 1988 § 1990s: Empirical Revolution § Corpus-based methods produce the first widely used tools § Deep linguistic analysis often traded for robust approximations § Empirical evaluation is essential § 2000s: Richer linguistic representations used in statistical approaches, scale to more data! § 2010s: you decide!
What is Nearby NLP? § Computational Linguistics § Using computational methods to learn more about how language works § We end up doing this and using it § Cognitive Science § Figuring out how the human brain works § Includes the bits that do language § Humans: the only working NLP prototype! § Speech? § Mapping audio signals to text § Traditionally separate from NLP, converging? § Two components: acoustic models and language models § Language models in the domain of stat NLP
Problem: Ambiguities § Headlines: § Enraged Cow Injures Farmer with Ax § Ban on Nude Dancing on Governor ’ s Desk § Teacher Strikes Idle Kids § Hospitals Are Sued by 7 Foot Doctors § Iraqi Head Seeks Arms § Stolen Painting Found by Tree § Kids Make Nutritious Snacks § Local HS Dropouts Cut in Half § Why are these funny?
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